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For facial expressions recognition, the traditional method is to execute feature extraction and recognize by machine learning. This method not only has complex feature extraction process but also poor generalization. In order to achieve better facial expression recognition, the paper proposes a facial expression recognition method combining feature extraction and convolutional neural network. Firstly, the AdaBoost algorithm based on Haar-like feature is used to detect the face region of the original image of the database, and then extract the local Binary Patterns (LBP) feature map of the face region, normalize the size and input it into the improved LeNet-5 network to recognize. The recognition rate is 98.19% and 96.35% respectively in the CK+ and JAFFE database with 10-fold Cross-validation method. The experimental results show that this method has certain advancement and effectiveness in facial expression recognition compared with other mainstream methods.